A Moving Target Recognition Algorithm Based on Improved Mixture Gaussian Background Model

被引:0
作者
Zhang Yongmei [1 ]
Ma Li [1 ]
Liu Mengmeng [2 ]
Sun Haiyan [1 ]
机构
[1] North China Univ Technol, Sch Comp Sci & Technol, Beijing, Peoples R China
[2] North China Univ Technol, Sch Elect Informat Engn, Beijing, Peoples R China
来源
PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON VIDEO AND IMAGE PROCESSING (ICVIP 2017) | 2017年
基金
中国国家自然科学基金;
关键词
moving target recognition; target detection; potential region; mixture Gaussian background model; multi-features;
D O I
10.1145/3177404.3177418
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Many mutant factors in the video such as noise and illumination can easily lead to the moving target recognition error. The paper proposes a moving target recognition algorithm based on improved mixture Gaussian background model for ships and vehicles. The algorithm on account of mixture Gaussian background model and three-frame difference can obtain the potential target regions with less background under the condition of motion disturbance and light mutation in the background, extract the straight line, shape factor and Zernike moment features from the potential regions, and construct the least square support vector machine to identify the ships and vehicles. The experiment results show the algorithm can accurately identify the ships and vehicles.
引用
收藏
页码:99 / 102
页数:4
相关论文
共 5 条
[1]  
Li Jun-bao, 2017, NAVIGATION POSITION, V1, P102, DOI [10.19306/j.cnki.2095-8110.2017.01.011, DOI 10.19306/J.CNKI.2095-8110.2017.01.011]
[2]   Image Classification with the Fisher Vector: Theory and Practice [J].
Sanchez, Jorge ;
Perronnin, Florent ;
Mensink, Thomas ;
Verbeek, Jakob .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2013, 105 (03) :222-245
[3]  
Sheng Jiachuan, 2015, Computer Engineering and Applications, V51, P176, DOI 10.3778/j.issn.1002-8331.1501-0399
[4]  
Xu Bing, 2016, Journal of Beijing University of Aeronautics and Astronautics, V42, P310, DOI 10.13700/j.bh.1001-5965.2015.0113
[5]  
Xu Chun-xiu, 2015, NETWORK SECURITY TEC, V294, P58, DOI [10.3969/j.issn.1009-6833.2015.01.104, DOI 10.3969/J.ISSN.1009-6833.2015.01.104]